A Systematic Strategy to Find Potential Therapeutic Targets for Pseudomonas aeruginosa Using Integrated Computational Models

dc.creatorMedeiros Filho, Fernando
dc.creatorNascimento, Ana Paula Barbosa do
dc.creatorCosta, Maiana de Oliveira Cerqueira e
dc.creatorMerigueti, Thiago Castanheira
dc.creatorMenezes, Marcio Argollo de
dc.creatorNicolás, Marisa Fabiana
dc.creatorSantos, Marcelo Trindade dos
dc.creatorAssef, Ana Paula D’Alincourt Carvalho
dc.creatorSilva, Fabrício Alves Barbosa da
dc.date2022-01-21T13:45:46Z
dc.date2022-01-21T13:45:46Z
dc.date2021
dc.date.accessioned2023-09-26T21:03:36Z
dc.date.available2023-09-26T21:03:36Z
dc.identifierMEDEIROS, FILHO, Fernando et al. A Systematic Strategy to Find Potential Therapeutic Targets for Pseudomonas aeruginosa Using Integrated Computational Models. Frontiers in Molecular Biosciences, v. 8, Article 728129, p. 1 - 14, Sept. 2021.
dc.identifier2296-889X
dc.identifierhttps://www.arca.fiocruz.br/handle/icict/50842
dc.identifier10.3389/fmolb.2021.728129
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8868480
dc.descriptionPseudomonas aeruginosa is an opportunistic human pathogen that has been a constant global health problem due to its ability to cause infection at different body sites and its resistance to a broad spectrum of clinically available antibiotics. The World Health Organization classified multidrug-resistant Pseudomonas aeruginosa among the topranked organisms that require urgent research and development of effective therapeutic options. Several approaches have been taken to achieve these goals, but they all depend on discovering potential drug targets. The large amount of data obtained from sequencing technologies has been used to create computational models of organisms, which provide a powerful tool for better understanding their biological behavior. In the present work, we applied a method to integrate transcriptome data with genome-scale metabolic networks of Pseudomonas aeruginosa. We submitted both metabolic and integrated models to dynamic simulations and compared their performance with published in vitro growth curves. In addition, we used these models to identify potential therapeutic targets and compared the results to analyze the assumption that computational models enriched with biological measurements can provide more selective and (or) specific predictions. Our results demonstrate that dynamic simulations from integrated models result in more accurate growth curves and flux distribution more coherent with biological observations. Moreover, identifying drug targets from integrated models is more selective as the predicted genes were a subset of those found in the metabolic models. Our analysis resulted in the identification of 26 non-host homologous targets. Among them, we highlighted five top-ranked genes based on lesser conservation with the human microbiome. Overall, some of the genes identified in this work have already been proposed by different approaches and (or) are already investigated as targets to antimicrobial compounds, reinforcing the benefit of using integrated models as a starting point to selecting biologically relevant therapeutic targets.
dc.formatapplication/pdf
dc.languageeng
dc.publisherFrontiers Media
dc.rightsopen access
dc.subjectPseudomonas aeruginosa
dc.subjectRede metabólica
dc.subjectDados de transcrição
dc.subjectModelo integrado
dc.subjectAlvo terapêutico
dc.subjectPseudomonas aeruginosa
dc.subjectMetabolic network
dc.subjectTranscriptome data
dc.subjectIntegrated model
dc.subjectTherapeutic target
dc.titleA Systematic Strategy to Find Potential Therapeutic Targets for Pseudomonas aeruginosa Using Integrated Computational Models
dc.typeArticle


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